Cleanup, removal of unmaintained code (#836)

* add dtype to Box

* remove board_game, debugging, safety, parameter_tuning environments

* massive set of breaking changes
- remove python logging module
- _step, _reset, _seed, _close => non underscored method
- remove benchmark and scoring folder

* Improve render("human"), now resizable, closable window.

* get rid of default step and reset in wrappers, so it doesn’t silently fail for people with underscore methods

* CubeCrash unit test environment

* followup fixes

* MemorizeDigits unit test envrionment

* refactored spaces a bit
fixed indentation
disabled test_env_semantics

* fix unit tests

* fixes

* CubeCrash, MemorizeDigits tested

* gym backwards compatibility patch

* gym backwards compatibility, followup fixes

* changelist, add spaces to main namespaces

* undo_logger_setup for backwards compat

* remove configuration.py

README.rst

@@ -4,7 +4,7 @@ OpenAI Gym
 **OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.** This is the ``gym`` open-source library, which gives you access to a standardized set of environments.
 
 .. image:: https://travis-ci.org/openai/gym.svg?branch=master
-	:target: https://travis-ci.org/openai/gym
+    :target: https://travis-ci.org/openai/gym
 
 `See What's New section below <#what-s-new>`_
 
@@ -126,7 +126,7 @@ fake display. The easiest way to do this is by running under
 
 .. code:: shell
 
-	 xvfb-run -s "-screen 0 1400x900x24" bash
+     xvfb-run -s "-screen 0 1400x900x24" bash
 
 Installing dependencies for specific environments
 -------------------------------------------------
@@ -173,18 +173,6 @@ The Atari environments are a variety of Atari video games. If you didn't do the
 
 This will install ``atari-py``, which automatically compiles the `Arcade Learning Environment <http://www.arcadelearningenvironment.org/>`_. This can take quite a while (a few minutes on a decent laptop), so just be prepared.
 
-Board games
------------
-
-The board game environments are a variety of board games. If you didn't do the full install, you can install dependencies via ``pip install -e '.[board_game]'`` (you'll need ``cmake`` installed) and then get started as follow:
-
-.. code:: python
-
-	  import gym
-	  env = gym.make('Go9x9-v0')
-	  env.reset()
-	  env.render()
-
 Box2d
 -----------
 
@@ -261,6 +249,17 @@ We are using `pytest <http://doc.pytest.org>`_ for tests. You can run them via:
 What's new
 ==========
 
+- 2018-01-25: Made some aesthetic improvements and removed unmaintained parts of gym. This may seem like a downgrade in functionality, but it is actually a long-needed cleanup in preparation for some great new things that will be released in the next month.
+
+    + Now your `Env` and `Wrapper` subclasses should define `step`, `reset`, `render`, `close`, `seed` rather than underscored method names.
+    + Removed the `board_game`, `debugging`, `safety`, `parameter_tuning` environments since they're not being maintained by us at OpenAI. We encourage authors and users to create new repositories for these environments.
+    + Changed `MultiDiscrete` action space to range from `[0, ..., n-1]` rather than `[a, ..., b-1]`.
+    + No more `render(close=True)`, use env-specific methods to close the rendering.
+    + Removed `scoreboard` directory, since site doesn't exist anymore.
+    + Moved `gym/monitoring` to `gym/wrappers/monitoring`
+    + Add `dtype` to `Space`.
+    + Not using python's built-in module anymore, using `gym.logger`
+
 - 2018-01-24: All continuous control environments now use mujoco_py >= 1.50.
   Versions have been updated accordingly to -v2, e.g. HalfCheetah-v2. Performance
   should be similar (see https://github.com/openai/gym/pull/834) but there are likely

examples/agents/cem.py

@@ -1,13 +1,9 @@
 from __future__ import print_function
 
 import gym
-from gym import wrappers
-import logging
+from gym import wrappers, logger
 import numpy as np
-try:
-    import cPickle as pickle
-except ImportError:
-    import pickle
+from six.moves import cPickle
 import json, sys, os
 from os import path
 from _policies import BinaryActionLinearPolicy # Different file so it can be unpickled
@@ -48,8 +44,7 @@ def do_rollout(agent, env, num_steps, render=False):
     return total_rew, t+1
 
 if __name__ == '__main__':
-    logger = logging.getLogger()
-    logger.setLevel(logging.INFO)
+    logger.set_level(logger.INFO)
 
     parser = argparse.ArgumentParser()
     parser.add_argument('--display', action='store_true')

examples/agents/keyboard_agent.py

@@ -1,18 +1,19 @@
 #!/usr/bin/env python
 from __future__ import print_function
 
-import sys, gym
+import sys, gym, time
 
 #
-# Test yourself as a learning agent! Pass environment name as a command-line argument.
+# Test yourself as a learning agent! Pass environment name as a command-line argument, for example:
+#
+# python keyboard_agent.py SpaceInvadersNoFrameskip-v4
 #
 
 env = gym.make('LunarLander-v2' if len(sys.argv)<2 else sys.argv[1])
 
 if not hasattr(env.action_space, 'n'):
     raise Exception('Keyboard agent only supports discrete action spaces')
 ACTIONS = env.action_space.n
-ROLLOUT_TIME = 1000
 SKIP_CONTROL = 0    # Use previous control decision SKIP_CONTROL times, that's how you
                     # can test what skip is still usable.
 
@@ -44,26 +45,36 @@ def rollout(env):
     human_wants_restart = False
     obser = env.reset()
     skip = 0
-    for t in range(ROLLOUT_TIME):
+    total_reward = 0
+    total_timesteps = 0
+    while 1:
         if not skip:
             #print("taking action {}".format(human_agent_action))
             a = human_agent_action
+            total_timesteps += 1
             skip = SKIP_CONTROL
         else:
             skip -= 1
 
         obser, r, done, info = env.step(a)
-        env.render()
+        if r != 0:
+            print("reward %0.3f" % r)
+        total_reward += r
+        window_still_open = env.render()
+        if window_still_open==False: return False
         if done: break
         if human_wants_restart: break
         while human_sets_pause:
             env.render()
-            import time
             time.sleep(0.1)
+        time.sleep(0.1)
+    print("timesteps %i reward %0.2f" % (total_timesteps, total_reward))
 
 print("ACTIONS={}".format(ACTIONS))
 print("Press keys 1 2 3 ... to take actions 1 2 3 ...")
 print("No keys pressed is taking action 0")
 
 while 1:
-    rollout(env)
+    window_still_open = rollout(env)
+    if window_still_open==False: break
+

examples/agents/random_agent.py

@@ -1,10 +1,8 @@
 import argparse
-import logging
 import sys
 
 import gym
-from gym import wrappers
-
+from gym import wrappers, logger
 
 class RandomAgent(object):
     """The world's simplest agent!"""
@@ -19,19 +17,9 @@ def act(self, observation, reward, done):
     parser.add_argument('env_id', nargs='?', default='CartPole-v0', help='Select the environment to run')
     args = parser.parse_args()
 
-    # Call `undo_logger_setup` if you want to undo Gym's logger setup
-    # and configure things manually. (The default should be fine most
-    # of the time.)
-    gym.undo_logger_setup()
-    logger = logging.getLogger()
-    formatter = logging.Formatter('[%(asctime)s] %(message)s')
-    handler = logging.StreamHandler(sys.stderr)
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-
-    # You can set the level to logging.DEBUG or logging.WARN if you
+    # You can set the level to logger.DEBUG or logger.WARN if you
     # want to change the amount of output.
-    logger.setLevel(logging.INFO)
+    logger.set_level(logger.INFO)
 
     env = gym.make(args.env_id)
 

gym/__init__.py

@@ -1,56 +1,17 @@
 import distutils.version
-import logging
 import os
 import sys
+import warnings
 
 from gym import error
-from gym.configuration import logger_setup, undo_logger_setup
 from gym.utils import reraise
 from gym.version import VERSION as __version__
 
-logger = logging.getLogger(__name__)
-
-# Do this before importing any other gym modules, as most of them import some
-# dependencies themselves.
-def sanity_check_dependencies():
-    import numpy
-    import requests
-    import six
-
-    if distutils.version.LooseVersion(numpy.__version__) < distutils.version.LooseVersion('1.10.4'):
-        logger.warn("You have 'numpy' version %s installed, but 'gym' requires at least 1.10.4. HINT: upgrade via 'pip install -U numpy'.", numpy.__version__)
-
-    if distutils.version.LooseVersion(requests.__version__) < distutils.version.LooseVersion('2.0'):
-        logger.warn("You have 'requests' version %s installed, but 'gym' requires at least 2.0. HINT: upgrade via 'pip install -U requests'.", requests.__version__)
-
-# We automatically configure a logger with a simple stderr handler. If
-# you'd rather customize logging yourself, run undo_logger_setup.
-#
-# (Note: this code runs before importing the rest of gym, since we may
-# print a warning at load time.)
-#
-# It's generally not best practice to configure the logger in a
-# library. We choose to do so because, empirically, many of our users
-# are unfamiliar with Python's logging configuration, and never find
-# their way to enabling our logging. Users who are aware of how to
-# configure Python's logging do have to accept a bit of incovenience
-# (generally by caling `gym.undo_logger_setup()`), but in exchange,
-# the library becomes much more usable for the uninitiated.
-#
-# Gym's design goal generally is to be simple and intuitive, and while
-# the tradeoff is definitely not obvious in this case, we've come down
-# on the side of auto-configuring the logger.
-
-if not os.environ.get('GYM_NO_LOGGER_SETUP'):
-    logger_setup()
-del logger_setup
-
-sanity_check_dependencies()
-
 from gym.core import Env, Space, Wrapper, ObservationWrapper, ActionWrapper, RewardWrapper
-from gym.benchmarks import benchmark_spec
 from gym.envs import make, spec
-from gym.scoreboard.api import upload
-from gym import wrappers
+from gym import wrappers, spaces, logger
+
+def undo_logger_setup():
+    warnings.warn("gym.undo_logger_setup is deprecated. gym no longer modifies the global logging configuration")
 
-__all__ = ["Env", "Space", "Wrapper", "make", "spec", "upload", "wrappers"]
+__all__ = ["Env", "Space", "Wrapper", "make", "spec", "wrappers"]